DeepSTAPLE: Learning to predict multimodal registration quality for unsupervised domain adaptation
While deep neural networks often achieve outstanding results on semantic segmentation tasks within a dataset domain, performance can drop significantly when predicting domain-shifted input data. Multi-atlas segmentation utilizes multiple available sample annotations which are deformed and propagated to the target domain via multimodal image registration and fused to a consensus label afterwards but subsequent network training with the registered data may not yield optimal results due to registration errors. In this work, we propose to extend a curriculum learning approach with additional regularization and fixed weighting to train a semantic segmentation model along with data parameters representing the atlas confidence. Using these adjustments we can show that registration quality information can be extracted out of a semantic segmentation model and further be used to create label consensi when using a straightforward weighting scheme. Comparing our results to the STAPLE method, we find that our consensi are not only a better approximation of the oracle-label regarding Dice score but also improve subsequent network training results.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationImage RegistrationSegmentationSemantic SegmentationUnsupervised Domain AdaptationSimilar Papers 제목 키워드 기반
Collaborative Learning for Unsupervised Multimodal Remote Sensing Image Registration: Integrating Self-Supervision and MIM-Guided Diffusion-Based Image Translation
The substantial modality-induced variations in radiometric, texture, and structural characteristics pose significant challenges for the accurate registration of multimodal images. While supervised deep learning methods h…
Image RegistrationPseudo LabelUnsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies
Multimodal image registration is a very challenging problem for deep learning approaches. Most current work focuses on either supervised learning that requires labelled training scans and may yield models that bias towar…
Domain AdaptationImage RegistrationUnsupervised Domain AdaptationUnsupervised learning of multimodal image registration using domain adaptation with projected Earth Mover’s discrepancies
Multimodal image registration is a very challenging problem for deep learning approaches. Most current work focuses on either supervised learning that requires labelled training scans and may yield models that bias towar…
Domain AdaptationImage RegistrationUnsupervised Domain AdaptationMultimodality Biomedical Image Registration using Free Point Transformer Networks
We describe a point-set registration algorithm based on a novel free point transformer (FPT) network, designed for points extracted from multimodal biomedical images for registration tasks, such as those frequently encou…
Image RegistrationUnimodal Cyclic Regularization for Training Multimodal Image Registration Networks
The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization. In the deep learning era, researchers proposed many approaches to auto…
Image Registration